Voyage 3.5 Lite
import { embed } from 'ai';
const result = await embed({ model: 'voyage/voyage-3.5-lite', value: 'Sunny day at the beach',})Providers
Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.
More models by Voyage AI
| Model |
|---|
| 32K | $0.12/M | — |  | 01/15/2026 | ||||||||
| 32K | $0.02/M | — | | 01/15/2026 | ||||||||
| 32K | $0.06/M | — | | 01/15/2026 | ||||||||
| 32K | $0.05/M | — | | 08/11/2025 | ||||||||
$0.06/M | — | | 05/20/2025 | |||||||||
$0.12/M | — | | 04/15/2024 |
About Voyage 3.5 Lite
Voyage 3.5 Lite is Voyage AI's cost-efficient embedding model, released May 20, 2025. It supports a context window of 0 tokens and produces embeddings in four dimensions: 2048, 1024, 512, and 256. Voyage 3.5 Lite outperforms OpenAI text-embedding-3-large by 6.34% and its predecessor voyage-3-lite by 4.28% on average across eight retrieval domains.
Voyage 3.5 Lite achieves retrieval quality within 0.3% of Cohere Embed v4 at one-sixth the cost. That makes it a practical choice for high-volume embedding workloads where per-token pricing matters. It supports the same Matryoshka dimensionality and quantization-aware training as the premium voyage-3.5, including 32-bit float, 8-bit integer, and binary precision formats. Binary rescoring yields up to 6.89% quality improvement.
If you run large-scale RAG pipelines or semantic search over millions of documents, Voyage 3.5 Lite keeps embedding infrastructure affordable without dropping to a lower quality tier. Voyage AI recommends it for cost-sensitive production deployments.
What To Consider When Choosing a Provider
- Configuration: Voyage 3.5 Lite costs one-third the price of voyage-3.5 while maintaining high retrieval quality. The accuracy gap is small enough that most production workloads will not see a meaningful difference in end-user outcomes.
- Configuration: If you embed millions of documents or process high query volumes, the per-token savings compound significantly. Voyage 3.5 Lite is designed for this scale.
- Configuration: Voyage 3.5 Lite supports the same dimensionality and precision options as voyage-3.5. Combine reduced dimensions with int8 or binary precision for maximum cost savings on very large indices.
- Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use Voyage 3.5 Lite
Best for
- High-volume RAG pipelines: Per-token embedding cost scales linearly with corpus size and query volume
- Semantic search: Retrieval within 0.3% of Cohere Embed v4 at one-sixth the cost across large document collections
- Startup and growth-stage teams: Production-quality embeddings without enterprise-tier pricing
- Prototyping and experimentation: Lower cost enables faster iteration on retrieval strategies
- Multi-domain retrieval: The same eight domains as voyage-3.5 at a lower price point
Consider alternatives when
- Retrieval accuracy is the main priority: Voyage-3.5 delivers the highest retrieval accuracy among Voyage 3.5 general-purpose options
- Your corpus is exclusively source code: Voyage-code-3 is purpose-built for that domain
- You need multimodal embeddings: Voyage-3.5-lite is text-only; pick a model with native image inputs when screenshots or diagrams go into the same index
- You require the absolute lowest latency: A smaller, faster model better fits tight SLAs
Conclusion
Voyage 3.5 Lite balances per-token cost and retrieval quality in Voyage AI's embedding lineup. It outperforms OpenAI text-embedding-3-large by 6.34% while matching Cohere Embed v4 quality at a fraction of the price. If you embed at scale, it gives you production-grade retrieval without overcommitting on infrastructure costs. Access it through AI Gateway for unified provider management.